Pedometers can be beneficial in increasing physical activity by providing real-time feedback to users. Step count, distance traveled, and calories burned are often recorded by pedometers, allowing users to set the fitness goals and see if they are achieving them. Accelerometer pedometers are shown to be more accurate in counting steps than traditional mechanical pedometers, Distance traveled is based on the stride length of the user and step count, which can be calculated using the data collected by the pedometer. Some pedometers also provide a number for calories burned, but how accurate is this value and how is it calculated? The accuracy of this quantity may be beneficial for users who are basing their daily caloric intake on the amount of calories they believe they have burned. The current formula used to determine calories burned is: total calories burned = Duration (in minutes)*(MET*0.0175*weight in kg). MET is a value based on the intensity of the exercise being performed, as can be seen in the chart depicted on the Hospital for Special Surgery website . How to accurately measure calories burned using a pedometer has not been determined. Heart rate can be a good way to determine intensity of exercise but not all pedometers include heart rate monitors. Some pedometers use the calculated speed of the user and their weight to estimate calories burned . Studies have shown that calories burned is often underestimated when using a pedometer. Compared to metabolic data collected during exercise from VO2, the pedometer calorie count, that utilized weight, number of steps, stride length, and speed of the user, was lower . The solution I propose is to implement a peak detection system that takes into account the amplitude of the acceleration waveform to correlate this to METs and calories burned.
Most accelerometer pedometers utilize an adaptive peak detection system to distinguish extraneous movements from steps. A peak detection system is implemented to determine if a peak in the acceleration waveform should actually count as a step. An amplitude for the peak and a time threshold for the wave are used to determine if the movement qualifies as a step. When the required conditions are met, the step is counted and the system begins to search for the next peak in the acceleration data . However, other than determining if the threshold value is met, the amplitude of the peak is not utilized. I propose that using the amplitude could provide a more accurate way of determining the intensity of the movement and could be applied to the calculation of calories burned. Although this will not be completely accurate, I believe that this would improve on the current method used in some pedometers to calculate calories burned. Pedometers that have heart rate monitors can use this data to more accurately predict energy expenditure but pedometers that do not often use crude calculations such as calories/kg/hr = 1.25 x speed (km/h) or calories/hr = 1 x weight (kg) while resting . If the amplitude data can be stored and processed, this information could be correlated to METs, which may improve accuracy in energy expenditure data.
In order to implement the peak magnitude system, the magnitude of all 3 axes could be found prior to filtering. Using the equation mag = sqrt(x^2+y^2+z^2), the overall magnitude of acceleration could be found for each movement. This data can then be filtered and smoothed returning a similar pattern as seen in figure 1 . A peak detection system functions by analyzing time periods and determining where the peak falls within that interval. When a peak is detected that fulfills the magnitude threshold and time threshold, the pedometer tracks a step. If the magnitude of the peak can be stored, this could be used to determine the MET value of an activity. METs range from 1 – 23, 1 occurring when you are sitting at rest and 23 occurring during extremely vigorous exercise, such as running at a 4:30 mile pace .
A block diagram, as seen in figure 2, could be used to correlate the magnitude to the intensity of exercise. This potential solution to the inaccuracies of energy expenditure calculated by pedometers still has some limitations. One limitation being that not all intense exercises will generate high acceleration values despite the large caloric expenditure. For example, squatting a one rep max may require a great deal of energy, but the pedometer may not pick it up as movement and therefore would not count it as a step. This is an example of where heart rate monitors may be beneficial in determining energy expenditure. Similarly, exercising on a stationary bike may be very intense but not trigger any steps to be counted if the pedometer is worn on the wrist and therefore would not be counted in the overall calorie burn for the user. This assumption that acceleration correlates directly to exercise intensity may accurately apply in all cases, but it could still improve the overall calculation of calories burned.
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